Network analysis and link prediction in competitive women's basketball

2026-01-30

AI Summary

This study investigates how network structures can be leveraged to predict future outcomes in competitive women’s basketball (NCAA and WNBA) and to understand player interactions. The authors construct contest, block, and passing networks and apply link prediction techniques combining structural similarity metrics—such as Common Out-Neighbors and PageRank—with graph embeddings (node2vec) and logistic regression. They introduce a novel “quiet leader” strength metric to capture the influence of low-visibility yet high-impact players. Experimental results demonstrate that embedding-based models significantly outperform baseline methods across multiple scenarios, confirming that higher-order network structures contain valuable predictive signals. While passing prediction exhibits comparatively lower performance, it offers strong interpretability, highlighting the trade-off between accuracy and explainability in modeling player dynamics.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Network structure and its role in prediction are examined in competitive basketball at the team and player levels. Adversarial game outcome networks from NCAA Division I women's basketball from 2021 to 2024 are used to compute the common out-neighbor score and PageRank, which are combined into a low-key leader strength that identifies competitors influential through structural similarity despite relatively low centrality. This measure is related to changes in NCAA NET rankings by grouping teams into quantiles and comparing average rank changes across seasons for both previous-to-current and current-to-next transitions. Link prediction is then studied using node2vec embeddings across three interaction settings. For NCAA regular-season game networks, cosine similarity between team embeddings is used in a logistic regression model to predict March Madness matchups. For WNBA shot-blocking networks, future directed blocking interactions are predicted via logistic regression on concatenated source-target player embeddings. For WNBA passing networks, region embeddings learned from first-quarter passes are evaluated for their ability to predict subsequent passing connections. Across NCAA and WNBA settings, embedding-based models provide statistically significant evidence that higher-order network structure contains predictive signals for future interactions, while the passing experiment shows weaker predictive performance but yields interpretable similarity patterns consistent with passing feasibility.

Problem

• link prediction
• network analysis
• competitive basketball
• team interactions
• player networks

Innovation

• network embedding
• link prediction
• low-key leader strength
• node2vec
• sports analytics